1. Field of the Invention
The present invention relates to a method and apparatus for inspecting a solder joint of a printed circuit board (PCB) and more particularly, to a method and apparatus for inspecting a solder joint using a correlation neural network, in which solder joint images are effectively classified regardless of positional shift within a pre-defined window, a smaller memory storage is required for synaptic weights, and a learning rate is improved. The memory improvements are achieved by using geometric correlation terms to represent the spatial relations of red, green and blue color patterns in an effective manner. The experiences of a human inspector were utilized in building the architecture of this neural network and those experiences have reduced the classification burden from that of a back propagation (BP) network, thereby improving the overall classification performance.
2. Description of the Related Art
Surface-mounting technology (SMT) in printed-circuit board assembly processes has been vigorously developing to keep pace with the new electrical products' trend toward the miniaturization of components and denser packing of boards. In the commercial PCB assembly process using SMT, a great deal of effort has been directed toward quality control which is crucial to ensure reliability in end products. However, one hundred percent accurate inspection is difficult for a human inspector due to high production rates and the tedious nature of manual inspection. For these reasons, the development of automatic visual inspection systems for solder joints has drawn increasing attention with increasing demand for reliability in end products.
Automated solder joint inspection, however, has been considered difficult due to the sheen of the solder joint surface and the complexity of solder joint shapes. These two effects make it difficult to extract three-dimensional (3-D) information of the solder joint surface. Specular reflections from the high sheen surface of the solder joint may appear, disappear, or change their patterns abruptly even with small changes in viewing direction. Furthermore, a distant point illumination can not produce smooth shading on the specular solder joint surface, because light is reflected in a single direction. Although many researchers have tackled this challenging problem, only a few commercial systems using sophisticated illumination sources, sensors and mechanisms are on the market. The wide variety of the solder joint shapes is a barrier to developing an automatic solder joint inspection system. Solder joint shapes tend to vary greatly in three dimensions, depending on soldering conditions such as the amount of solder paste cream and the heating level during the soldering process.
To extract three-dimensional shape information for solder joint inspection, a tiered-color illumination system is used. The tiered-color illumination system, which consists of red, green, and blue color circular lamps and one color camera, is well known in the art. Lit by such a system, variations in inclination of the solder joint surface reveal three patterns of highlight color, that is, one each in red, green and blue. The spatial relations of the highlight color patterns provide visual cues for inferring a three-dimensional shape of the inspected surface. However, due to the complexity of the solder joint shapes, the geometric characteristics of the color patterns, imaged by the tiered-color illumination system, also exhibit substantial variability. This makes it difficult to classify the color patterns of the solder joints into distinct groups. Even if the solder joints belong to the same class of acceptable quality, their shapes are different from each other to some extent. In addition, there is no quantitative reference of solder joint shapes to decide their soldering quality. Classification criteria for solder joint inspection usually depend on a human's skillful experience which is difficult to program. Due to this complexity, careful attention is required to define suitable visual features and to determine classification criteria.
During the last several years, artificial intelligent approaches have been applied to many problems due to their ability to learn from a human's experience. Multi-layered neural networks have been used vigorously during this trend. However, when the input data space is too complex to determine decision surfaces for classification, or when input data are shifted in position, etc., application of the multi-layered neural network suffers. In these situations, the convergence rate of the generalized-delta rule (G.D.R.) is often unacceptably low, and often the classification may not be successful.